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Machine Learning in Medical Imaging

  • Kartonierter Einband
  • 724 Seiten
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This book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held i... Weiterlesen
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This book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.*

The 71 papers presented in this volume were carefully reviewed and selected from 92 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.

*The workshop was held virtually.

Contrastive Representations for Continual Learning of Fine-grained Histology Images.- Learning Transferable 3D-CNN for MRI-based Brain Disorder Classification from Scratch: An Empirical Study.- Knee Cartilages Segmentation Based on Multi-scale Cascaded Neural Networks.- Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation.- Interpretable Histopathology Image Diagnosis via Whole Tissue Slide Level Supervision.- Variational Encoding and Decoding for Hybrid Supervision of Registration Network.- Multiresolution Registration Network (MRN) Hierarchy with Prior Knowledge Learning.- Learning to Synthesize 7T MRI from 3T MRI with Few Data by Deformable Augmentation.- Rethinking Pulmonary Nodule Detection in Multi-view 3D CT Point Cloud Representation.- End-to-end lung nodule detection framework with model-based feature projection block.- Learning Structure from Visual SemanticFeatures and Radiology Ontology for LymphNode Classification on MRI.- Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment.- Cell Counting by a Location-Aware Network.- Exploring Gyro-Sulcal Functional Connectivity Differences across Task Domains via Anatomy-Guided Spatio-Temporal Graph Convolutional Networks.- StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis.- Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-ray Images.- Transfer learning with a layer dependent regularization for medical image segmentation.- Multi-Scale Self-Supervised Learning for Multi-Site Pediatric Brain MR Image Segmentation with Motion/Gibbs Artifacts.- Deep active learning for dual-view mammogram analysis.- Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound.- Semi-supervised Learning Regularized by Adversarial Perturbation and Diversity Maximization.- TransforMesh: A Transformer Network for Longitudinal Modeling of Anatomical Meshes.- A Recurrent Two-stage Anatomy-guided Network for Registration of Liver DCE-MRI.- Learning Infancy Brain Developmental Connectivity for the Cognitive Score Prediction.- Hierarchical 3D Feature Learning for Pancreas Segmentation.- Voxel-wise Cross-Volume Representation Learning for 3D Neuron Reconstruction.- Diagnosis of Hippocampal Sclerosis from Clinical Routine Head MR Images using Structure-Constrained Super-Resolution Network.- U-Net Transformer: Self and Cross Attention for Medical Image Segmentation.- Pre-biopsy multi-class classification of breast lesion pathology in mammograms.- Co-Segmentation of Multi-Modality Spinal Images Using Channel and Spatial Attention.- Hetero-Modal Learning and Expansive Consistency Constraints for Semi-Supervised Detection from Multi-Sequence Data.- STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains.- Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment.- MIST GAN: Modality Imputation using Style Transfer for MRI.- Biased Extrapolation in Latent Space for Imbalanced Deep Learning.- 3DMeT: 3D Medical Image Transformer for Knee Cartilage Defect Assessment.- A Gaussian Process Model for Unsupervised Analysis of High Dimensional Shape Data.- Standardized Analysis of Kidney Ultrasound Images for the Prediction of Pediatric Hydronephrosis Severity.- Automated deep learning-based detection of osteoporotic fractures in CT images.- GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation.- Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis.- Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling.- TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising.- Self-supervised Mean Teacher for Semi-supervisedChest X-ray Classification.- VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning.- Using Spatio-Temporal Correlation based Hybrid Plug-and-Play Priors (SEABUS) for Accelerated Dynamic Cardiac Cine MRI.- Window-Level is a Strong Denoising Surrogate.- Cardiovascular disease risk improves COVID-19 patient outcome prediction.- Self-Supervision Based Dual-Transformation Learning for Stain Normalization, Classification and Segmentation.- Deep Representation Learning for Image-Based Cell Profiling.- Detecting Extremely Small Lesions with Point Annotations via Multi-task Learning.- Morphology-guided Prostate MRI Segmentation with Multi-slice Association.- Unsupervised Cross-modality Cardiac Image Segmentation via Disentangled Representation Learning and Consistency Regularization.- Landmark-Guided Rigid Registration for Temporomandibular Joint MRI-CBCT Images with Large Field-of-View Difference.- Spine-rib Segmentation and Labeling via Hierarchical Matching and Rib-guided Registration.- Multi-scale Segmentation Network for Rib Fracture Classification from CT Images.- Knowledge-guided Multiview Deep Curriculum Learning for Elbow Fracture Classification.- Contrastive Learning of Single-Cell Phenotypic Representations for Treatment Classification.- CorLab-Net: Anatomical Dependency-Aware Point-Cloud Learning for Automatic Labeling of Coronary Arteries.- A Hybrid Deep Registration of MR Scans to Interventional Ultrasound for Neurosurgical Guidance.- Segmentation of Peripancreatic Arteries in Multispectral Computed Tomography Imaging.- SkullEngine: A Multi-Stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection.- Skull Segmentation from CBCT Images via Voxel-based Rendering.- Alzheimer's Disease Diagnosis via Deep Factorization Machine Models.- 3D Temporomandibular Joint CBCT Image Segmentation via Multi-directional Resampling Ensemble Learning Network.- Vox2Surf: Implicit Surface Reconstruction from Volumetric Data.- Clinically Correct Report Generation from Chest X-rays using Templates.- Extracting Sequential Features from Dynamic Connectivity Network with rs-fMRI Data for AD Classification.- Integration of Handcrafted and Embedded Features from Functional Connectivity Network with rs-fMRI for Brain Disease Classification.- Detection of Lymph Nodes in T2 MRI using Neural Network Ensembles.- Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection.


Titel: Machine Learning in Medical Imaging
Untertitel: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings
EAN: 9783030875886
ISBN: 3030875881
Format: Kartonierter Einband
Herausgeber: Springer International Publishing
Anzahl Seiten: 724
Gewicht: 1077g
Größe: H235mm x B155mm x T38mm
Jahr: 2021
Untertitel: Englisch
Auflage: 1st ed. 2021

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